https://psyarxiv.com/6wh9m/
How does the difficulty of a task affect people’s enjoyment and engagement? Intrinsic motivation and flow theories posit a ‘goldilocks’ optimum where task difficulty matches performer skill, yet current work is confounded by questionable measurement practices and lacks scalable methods to manipulate objective difficulty-skill ratios. We developed a 2-player tactical game test suite with an AI-controlled opponent that uses a variant of the Monte Carlo Tree Search algorithm to precisely manipulate difficulty-skill ratios. A pre-registered study (n=311) showed that our AI produced targeted difficulty-skill ratios without participants noticing the manipulation, yet different ratios had no significant impact on enjoyment or engagement. This indicates that difficulty-skill balance does not always affect engagement and enjoyment, but that games with AI-controlled difficulty provide a useful paradigm for rigorous future work on this issue
Professor at George Washington University
I am a Professor with Human-Technology Collaboration and Educational Technology programs at George Washington University in Washington DC. I have written 12 books and more than 100 articles, and I co-host of the Parsing Science podcast where scientists tell the stories behind their research. I am also the developer of the WeShareScience.com online platform for sharing research videos, and SciencePods.com where researchers can create free podcasts about their science. My research interests include human interactions with intelligent machines, needs, needs assessments, and instructional design.
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